Driving Factors and Scale Effects of Residents' Willingness to Pay for Environmental Protection under the Impact of COVID-19

In recent years, environmental degradation and the COVID-19 pandemic have seriously affected economic development and social stability. Addressing the impact of major public health events on residents' willingness to pay for environmental protection (WTPEP) and analyzing the drivers are necessary for improving human well-being and environmental sustainability. We designed a questionnaire to analyze the change in residents' WTPEP before and during COVID-19 and an established ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), and multiscale GWR to explore driver factors and scale effects of WTPEP based on the theory of environment Kuznets curve (EKC). The results show that (1) WTPEP is 0–20,000 yuan before COVID-19 and 0–50,000 yuan during COVID-19. Residents' WTPEP improved during COVID-19, which indicates that residents' demand for an ecological environment is increasing;(2) The shapes and inflection points of the relationships between income and WTPEP are spatially heterogeneous before and during COVID-19, but the northern WTPEP is larger than southern, which indicates that there is a spatial imbalance in WTPEP;(3) Environmental degradation, health, environmental quality, and education are WTPEP's significant macro-drivers, whereas income, age, and gender are significant micro-drivers. Those factors can help policymakers better understand which factors are more suitable for macro or micro environmental policy-making and what targeted measures could be taken to solve the contradiction between the growing ecological environment demand of residents and the spatial imbalance of WTPEP in the future.

Analysing the factors affecting medical waste generation in China

Medical waste deserves particular attention due to its potential for causing serious damage to people and the environment. Although the factors influencing the generation of medical waste are critical for designing policies aimed at effectively reducing medical waste and improving medical waste management, they have not been extensively studied. The rapid development of China's medical and health services and the sudden outbreak of Covid-19 have brought significant challenges to managing medical waste in China. Therefore, based on panel data from eight cities in China from 2013 to 2019, this study used a fixed-effects model to investigate the influencing factors of medical waste generation (MWG) in China, and tested the environmental Kuznets curve (EKC) hypothesis. The results show that there is a non-linear N-shaped curve relationship between MWG and per capita gross domestic product (GDP);MWG will continue to increase with economic growth, but the growth rate will slow down from fast to slow, and then from slow to fast with economic growth. The analysis also reveals that implementing a tiered diagnosis and treatment policy may negatively affect MWG by reducing the waste of medical resources and thus reducing the generation of medical waste. The positive effect of population size on MWG is also highly significant, so when the aging of the population increases, the generation of medical waste also increases. The three policy suggestions are provided: 1) improve the disposal capacity and efficiency of medical waste;2) give full play to the advantages of hierarchical diagnosis and treatment policy;3) improve the management level of medical waste in primary medical institutions. © 2023

Analysing the factors affecting medical waste generation in China

Medical waste deserves particular attention due to its potential for causing serious damage to people and the environment. Although the factors influencing the generation of medical waste are critical for designing policies aimed at effectively reducing medical waste and improving medical waste management, they have not been extensively studied. The rapid development of China's medical and health services and the sudden outbreak of Covid-19 have brought significant challenges to managing medical waste in China. Therefore, based on panel data from eight cities in China from 2013 to 2019, this study used a fixed-effects model to investigate the influencing factors of medical waste generation (MWG) in China, and tested the environmental Kuznets curve (EKC) hypothesis. The results show that there is a non-linear N-shaped curve relationship between MWG and per capita gross domestic product (GDP);MWG will continue to increase with economic growth, but the growth rate will slow down from fast to slow, and then from slow to fast with economic growth. The analysis also reveals that implementing a tiered diagnosis and treatment policy may negatively affect MWG by reducing the waste of medical resources and thus reducing the generation of medical waste. The positive effect of population size on MWG is also highly significant, so when the aging of the population increases, the generation of medical waste also increases. The three policy suggestions are provided: 1) improve the disposal capacity and efficiency of medical waste;2) give full play to the advantages of hierarchical diagnosis and treatment policy;3) improve the management level of medical waste in primary medical institutions.

Dynamic relationships among green bonds, CO2 emissions, and oil prices

Green bonds play a pivotal role in the financing of sustainable infrastructure systems. Likewise, CO2 emissions and oil prices can cause an impact on the green bonds market. In order to better understand this issue, this study analyzes the relationship among green bonds, CO2 futures' prices, and oil prices using a daily data set that includes 2,206 observations corresponding to daily information from 1 January 2014 to 15 June 2022. The Granger Causality Test and the Dynamic Conditional Correlation (DCC-Garch) Model were employed to conduct this analysis. Furthermore, a sensitivity analysis was performed to identify crisis periods concerning the sample period and provide an analysis of DCC-Garch results during extreme market conditions like the COVID-19 pandemic and the Russian invasion of Ukraine. The Granger Causality Test results present a unidirectional causality running from the Green Bond Index to the oil price returns. Also, there is a unidirectional causality running from the Green Bond Index to the CO2 futures' returns. Additionally, a unidirectional causality runs from the oil price returns to the CO2 futures' returns. The results for the DCC-Garch indicate a positive dynamic correlation between the Brent oil price return and the CO2 futures' returns. Finally, the Green Bond Index shows a negative dynamic correlation to the oil return and the CO2 futures' returns presenting a strong correlation in uncertainty periods.

Green innovation, resource price and carbon emissions during the COVID-19 times: New findings from wavelet local multiple correlation analysis

This paper investigates how oil price, COVID-19, and global energy innovation can affect carbon emissions under time-and frequency-varying perspectives. We contribute to the literature by being the first research to document the relationship between these variables in the short and long run (dynamically) at different frequencies in a multivariate context, thus providing a more detailed picture of the forces driving CO2 emissions. For this purpose, we use a novel methodology, i.e., the wavelet local multiple correlation (WLMC) recently developed by Polanco-Martinez et al. (2020). The results provide fresh evidence of long-run asymmetric dynamic correlations, highlighting how the oil price plays a key role in the dynamics of CO2 emissions. Moreover, we find that, during the long period, there is a strong negative co-movement between CO2 and the global energy innovation index, i. e., more investment in clean energy induces less emission. Supported by our findings, this research suggests crucial policy implications and insights for the governments worldwide in their efforts to revive their economies amidst the pandemic and environmental uncertainties.

Variations in the Yamuna River Water Quality During the COVID-19 Lockdowns

The COVID-19 pandemic that emerged in Wuhan city of China in December 2019 has adversely impacted the health and the economy, society, and other significant spheres of the human environment. The pandemic has severely impacted economic activities, especially the industrial production, transportation, tourism, and hoteling industries. The present study analyses the impact of varying severity of lockdowns of economic activities during various phases of the pandemic on the water quality of the Yamuna river on parameters like pH values, biological oxygen demand, chemical oxygen demand, dissolved oxygen, total suspended solids, and electrical conductivity. The study has found a significant improvement in water quality parameters with closing economic activities during lockdowns. The average levels of concentration of these parameters of water quality were quite low during the lockdown period at 7.26 (pH value), 31.32, 136.07, 7.93, 30.33 mg/L, and 1500.24 mu S/cm compared to pre lockdown periods levels at 7.53 (pH), 39.62, 116.52, 6.1, 57.2 mg/L and 1743.01 mu S/cm for biological oxygen demand, chemical oxygen demand, dissolved oxygen, total suspended solids, and electrical conductivity, respectively. In addition, the study has found a strong significant positive correlation between COD with BOD and TSS during the lockdown period. The major findings from the present study could be instrumental in making environmentally sustainable policies for the country's economic development. There is also a huge scope of scaling up of the study at the national level to analyze the health of the rivers in the backdrop of lockdowns.

Impact of work resumption on air quality after subsiding of COVID-19: evidence from China

This paper explores the short-run impact of work resumption, extensively launched on February 10, 2020 in China, on air quality after the subsiding of COVID-19. Utilizing the data of 1012 air-quality monitoring sites in 233 cities derived from the Real-time Release Air Quality Platform and the difference-in-differences method, we find that alternative measures of air quality index in non-Hubei provinces increase significantly, compared with those in Hubei province which was temporarily not allowed work resumption due to the severity of epidemic. Specifically, our results reveal a rise in AQI of 11.28 per cent, in PM2.5 of 12.47 per cent, in PM10 of 10.49 per cent, and in NO2 of 23.64 per cent, relative to the baseline mean. Moreover, the deterioration of air quality is found to be caused by intracity rather than intercity migration.

Economic Growth, Energy Use, and Greenhouse Gases Emission in Macao SAR, China

A city's economic structure and energy mix would change when the city is developed to accommodate more residents, visitors, and activities. This paper reviews Macao's economic growth, energy use, and greenhouse gases (GHG) emission from 1985 to 2020. Specifically, Macao's gross domestic product (GDP), energy use, and GHG emission have surged after the gaming industry was liberalized in 2002. The official data show that Macao's GDP was MOP 11 billion in 1985, increased by four-fold to MOP 54 billion in 2000, and then surged rapidly to MOP 445 billion in 2019. Additionally, Macao's total energy use increased from 8,840TJ in 1985 to 48,330TJ in 2019 while Macao's GHG emission increased from 0.70Mt of CO2-equivalent in 1985 to 6.13Mt of CO2-equivalent in 2019. Macao's GHG emission from all local sources per capita and GDP per capita exhibit an inverted U-shaped relationship, showing an environmental Kuznets curve. Due to the negative impact of COVID-19 pandemic, Macao's GDP dropped by 56% to MOP 194 billion while its total energy use and GHG emission dropped by 33% and 17% to 32,198TJ and 5.06Mt of CO2-equivalent, respectively, in 2020. © 2022